Apparatuses, systems, and methods for inferring a driving environment based on vehicle occupant actions

ABSTRACT

Apparatuses, systems and methods are provided for inferring a driving environment. More particularly, apparatuses, systems and methods are provided for inferring a driving environment based on vehicle occupant actions. Vehicle occupant actions may be based on digital image data.

CROSS REFERENCE TO RELATED APPLICATION

The present application is a continuation of U.S. patent applicationSer. No. 15/181,658, entitled APPARATUSES, SYSTEMS, AND METHODS FORINFERRING A DRIVING ENVIRONMENT BASED ON VEHICLE OCCUPANT ACTIONS, filedJun. 14, 2016, the disclosure of which is incorporated herein in itsentirety by reference.

TECHNICAL FIELD

The present disclosure is directed to apparatuses, systems and methodsfor inferring a driving environment. More particularly, the presentdisclosure is directed to apparatuses, systems and methods for inferringa driving environment based on vehicle occupant actions.

BACKGROUND

Vehicles are being provided with more complex systems. For example,vehicles commonly include a plethora of entertainment systems, such asstereos, USB interfaces for mobile telephones, video players, etc.Vehicles often have a host of other operator interfaces, such asemergency calling systems, vehicle navigation systems, heating and airconditioning systems, interior and exterior lighting controls, air bags,seatbelts, etc.

Vehicle operating environments are becoming more complex as well. Forexample, some roadways include u-turn lanes, round-a-bouts, no-leftturn, multiple lanes one way in the morning and the other way in theafternoon, etc. Increases in traffic are also contributing to increasedcomplexity.

These additional complexities contribute to increases in driver risk.What is needed are methods and systems for generating datarepresentative of vehicle driving environments.

SUMMARY

A device for inferring a vehicle driving environment may include apreviously classified image data receiving module stored on a memorythat, when executed by a processor, causes the processor to receivepreviously classified image data from at least one previously classifiedimage database. The previously classified image data may berepresentative of previously classified vehicle occupant actions. Thedevice may also include a current image data receiving module stored ona memory that, when executed by a processor, causes the processor toreceive current image data from at least one vehicle interior sensor.The current image data may be representative of current vehicle occupantaction. The device may further include a vehicle driving environmentdetermination module stored on a memory that, when executed by aprocessor, causes the processor to infer a vehicle driving environmentbased on a comparison of the current image data with the previouslyclassified image data.

In another embodiment, a computer-implemented method for inferring avehicle driving environment may include receiving, at a processor of acomputing device, previously classified image data from at least onepreviously classified image database in response to the processorexecuting a previously classified image data receiving module. Thepreviously classified image data may be representative of previouslyclassified vehicle occupant actions. The method may also includereceiving, at a processor of a computing device, current image data fromat least one vehicle interior sensor a current image data receivingmodule, in response to the processor executing a current image datareceiving module. The current image data may be representative ofcurrent vehicle occupant actions. The method may further includeinferring, using a processor of a computing device, a vehicle drivingenvironment, based on a comparison of the current image data with thepreviously classified image data, in response to the processor executinga vehicle driving environment determination module.

In a further embodiment, a non-transitory computer-readable mediumstoring computer-readable instructions that, when executed by aprocessor, cause the processor to inferring a vehicle drivingenvironment may include a previously classified image data receivingmodule that, when executed by a processor, causes the processor toreceive previously classified image data from at least one previouslyclassified image database. The previously classified image data may berepresentative of previously classified vehicle occupant actions. Thenon-transitory computer-readable medium may also include a current imagedata receiving module that, when executed by a processor, causes theprocessor to receive current image data from at least one vehicleinterior sensor. The current image data may be representative of currentvehicle occupant actions. The method may further include a vehicledriving environment determination module that, when executed by aprocessor, causes the processor to infer a vehicle driving environmentbased on a comparison of the current image data with the previouslyclassified image data.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 depicts an example report for a vehicle in-cabin insurance riskevaluation;

FIG. 2 depicts a high-level block diagram for an example computer systemfor generating data representative of vehicle in-cabin insurance riskevaluations;

FIGS. 3A and 3B depict block diagrams of example vehicle devises for usein generating data representative of a vehicle driving environment;

FIG. 4 depicts a block diagram of an example remote computing device foruse in generating data representative of vehicle in-cabin insurance riskevaluations;

FIGS. 5A and 5B depict flow diagrams for example methods of generatingdata representative of a vehicle driving environment;

FIG. 6 depicts a flow diagram for an example method of generating datarepresentative of a vehicle driver's actions along with an associatedtime stamp;

FIG. 7 depicts a flow diagram for an example method of generating datarepresentative of a prediction of a vehicle driver's action;

FIGS. 8-10 depict flow diagrams for example methods for trackingmovement of a vehicle driver's upper body;

FIG. 11 depicts an example sequence diagram for generating a report fora vehicle in-cabin insurance risk evaluation;

FIG. 12 depicts a detailed example E-R diagram for generating datarepresentative of a vehicle driver's actions along with an associatedtime stamp;

FIGS. 13A and 13B depict a flow diagram for an example method of adevelopment environment for generating data representative of vehiclein-cabin insurance risk evaluations;

FIG. 14 depicts an example computer system for development of a modelfor generating data representative of vehicle in-cabin insurance riskevaluations;

FIG. 15 depicts a block diagram of various components for development ofa model for generating data representative of vehicle in-cabin insurancerisk evaluations;

FIG. 16 depicts a block diagram for an example server side system forgenerating data representative of vehicle in-cabin insurance riskevaluations;

FIG. 17 depicts a flow diagram for example method of directlyinteracting with the SDK to obtain driver data for use in generatingdata representative of vehicle in-cabin insurance evaluations;

FIG. 18 depicts an example skeletal figure representative of a vehicledriver's upper body position;

FIG. 19 depicts an example object design for a database for use ingenerating data representative of vehicle in-cabin insurance riskevaluations; and

FIGS. 20-23 depict various example class diagrams of objects for use ina database of FIG. 19.

DETAIL DESCRIPTION

Apparatuses, systems and methods for generating data representative of avehicle driving environment may include the following capabilities: 1)determine whether a vehicle driver is looking at a road (i.e., trackingthe driver's face/eyes, with emphasis on differentiating between similaractions, such as a driver who is adjusting a radio while looking at theroad versus adjusting the radio while not looking at the road at all);2) determine whether a driver's hands are empty (e.g., includingdetermining an approximate size/shape of object in a driver's hands to,for example, differentiate between a cell phone and a large cup, forexample); 3) identify a finite number of driver postures; and 4) loggingrotated and scaled postures that are normalized for a range of differentdrivers.

An associated mobile application may accommodate all popular platforms,such as iOS, Android and Windows, to connect an onboard device to a cellphone. In addition, to act as data connection provider to remoteservers, the mobile application may provide a user friendly interfacefor reporting and troubleshooting. Accordingly, associated memory,processing, and related data transmission requirements are reducedcompared to previous approaches.

Turning to FIG. 1, an example report 100, representative of vehiclein-cabin insurance risk evaluation, is depicted. The report 100 mayinclude a title 105 (e.g., In-Cabin Risk Evaluation Report), aphotograph of a driver 110, a name of a driver 111, and a driveidentification 115 including, for example, a calendar date 116 and atime 117. The report 100 may also include value 121 (e.g., 67centimeters) for a range of movement of most distinct postures 120. Thereport 100 is a chronological diagram 130 of various driver postures129, 131, 132, 133, 134, 135 including details of a driver posture thatthe driver was in for the longest total time 125. The driver posturethat the driver was in for the longest total time 125 may include askeletal FIG. 126 representing the posture, a total elapsed time 127,and a number of individual occurrences of the posture 128. The report100 may further include a graph 140 (e.g., a bar chart) including atitle (e.g., posture vs. time distribution), a number of times a givenposture was determined 142, and a total time in a given posture 143. Thereport 100 may also include the top five postures during an associatedride 145 including skeletal figures representative of the respectivepostures 150, 155, 160, 165, 170, a time in any given posture during anassociated ride 151, 156, 161, 166, 171, and a number of occurrences ofany given posture during an associated ride 152, 157, 162, 167, 172.

With reference to FIG. 2, a high-level block diagram of vehicle in-cabinsystem 200 is illustrated that may implement communications between avehicle in-cabin device 205 and a remote computing device 210 (e.g., aremote server) to provide vehicle in-cabin device 205 location and/ororientation data, and vehicle interior occupant position data to, forexample, an insurance related database 270. The vehicle in-cabin system200 may acquire data from a vehicle in-cabin device 205 and generatethree dimensional (3D) models of a vehicle interior and occupants withinthe vehicle interior. The vehicle in-cabin system 200 may also acquiredata from a microphone 251, 252 and determine a source of sound andvolume of sound within a vehicle interior.

For clarity, only one vehicle in-cabin device 205 is depicted in FIG. 2.While FIG. 2 depicts only one vehicle in-cabin device 205, it should beunderstood that any number of vehicle in-cabin devices 205 may besupported. The vehicle in-cabin device 205 may include a memory 220 anda processor 225 for storing and executing, respectively, a module 221.The module 221, stored in the memory 220 as a set of computer-readableinstructions, may be related to a vehicle interior and occupant positiondata collecting application that, when executed on the processor 225,causes vehicle in-cabin device location data to be stored in the memory220. Execution of the module 221 may also cause the processor 225 togenerate at least one 3D model of at least a portion of a vehicleoccupant (e.g., a driver and/or passenger) within the vehicle interior.Execution of the module 221 may further cause the processor 225 toassociate the vehicle in-cabin device location data with a time and, ordate. Execution of the module 221 may further cause the processor 225 tocommunicate with the processor 255 of the remote computing device 210via the network interface 230, the vehicle in-cabin devicecommunications network connection 231 and the wireless communicationnetwork 215.

The vehicle in-cabin device 205 may also include a compass sensor 227, aglobal positioning system (GPS) sensor 229, and a battery 223. Thevehicle in-cabin device 205 may further include an image sensor input235 communicatively connected to, for example, a first image sensor 236and a second image sensor 237. While two image sensors 236, 237 aredepicted in FIG. 2, any number of image sensors may be included within avehicle interior monitoring system and may be located within a vehicleinterior. The vehicle in-cabin device 205 may also include an infraredsensor input 240 communicatively connected to a first infrared sensor241 and a second infrared sensor 242. While two infrared sensors 241,242 are depicted in FIG. 2, any number of infrared sensors may beincluded within a vehicle interior monitoring system and may be locatedwithin a vehicle interior. The vehicle in-cabin device 205 may furtherinclude an ultrasonic sensor input 245 communicatively connected to afirst ultrasonic sensor 246 and a second ultrasonic sensor 247. Whiletwo ultrasonic sensors 246, 247 are depicted in FIG. 2, any number ofultrasonic sensors may be included within a vehicle interior monitoringsystem and may be located within a vehicle interior. The vehiclein-cabin device 205 may also include a microphone input 250communicatively connected to a first microphone 251 and a secondmicrophone 252. While two microphones 251, 252 are depicted in FIG. 2,any number of microphones may be included within a vehicle interiormonitoring system and may be located within a vehicle interior. Thevehicle in-cabin device 205 may further include a display/user inputdevice 225.

As one example, a first image sensor 236 may be located in a driver-sideA-pillar, a second image sensor 237 may be located in a passenger-sideA-pillar, a first infrared sensor 241 may be located in a driver-sideB-pillar, a second infrared sensor 242 may be located in apassenger-side B-pillar, first and second ultrasonic sensors 246, 247may be located in a center portion of a vehicle dash and first andsecond microphones 251, 252 may be located on a bottom portion of avehicle interior rearview mirror. The processor 215 may acquire positiondata from any one of, or all of, these sensors 236, 237, 241, 242, 246,247, 251, 252 and generate at least one 3D model (e.g., a 3D model of atleast a portion of a vehicle driver) based on the position data. Theprocessor 215 may transmit data representative of at least one 3D modelto the remote computing device 210. Alternatively, the processor 215 maytransmit the position data to the remote computing device 210 and theprocessor 255 may generate at least one 3D model based on the positiondata. In either event, the processor 215 or the processor 255 retrievedata representative of a 3D model of a vehicle operator and compare thedata representative of the 3D model of at least a portion of the vehicledriver with data representative of at least a portion of the 3D modelvehicle operator. The processor 215 and, or the processor 255 maygenerate a vehicle driver warning based on the comparison of the datarepresentative of the 3D model of at least a portion of the vehicledriver with data representative of at least a portion of the 3D modelvehicle operator to warn the vehicle operator that his position isindicative of inattentiveness. Alternatively, the processor 215 and/orthe processor 255 may generate an advisory based on the comparison ofthe data representative of the 3D model of at least a portion of thevehicle driver with data representative of at least a portion of the 3Dmodel of a vehicle operator to advise the vehicle operator how tocorrect her position to improve attentiveness.

The network interface 230 may be configured to facilitate communicationsbetween the vehicle in-cabin device 205 and the remote computing device210 via any hardwired or wireless communication network 215, includingfor example a wireless LAN, MAN or WAN, WiFi, the Internet, or anycombination thereof. Moreover, the vehicle in-cabin device 205 may becommunicatively connected to the remote computing device 210 via anysuitable communication system, such as via any publicly available orprivately owned communication network, including those that use wirelesscommunication structures, such as wireless communication networks,including for example, wireless LANs and WANs, satellite and cellulartelephone communication systems, etc. The vehicle in-cabin device 205may cause insurance risk related data to be stored in a remote computingdevice 210 memory 260 and/or a remote insurance related database 270.

The remote computing device 210 may include a memory 260 and a processor255 for storing and executing, respectively, a module 261. The module261, stored in the memory 260 as a set of computer-readableinstructions, facilitates applications related to determining a vehiclein-cabin device location and/or collecting insurance risk related data.The module 261 may also facilitate communications between the computingdevice 210 and the vehicle in-cabin device 205 via a network interface265, a remote computing device network connection 266 and the network215 and other functions and instructions.

The computing device 210 may be communicatively coupled to an insurancerelated database 270. While the insurance related database 270 is shownin FIG. 2 as being communicatively coupled to the remote computingdevice 210, it should be understood that the insurance related database270 may be located within separate remote servers (or any other suitablecomputing devices) communicatively coupled to the remote computingdevice 210. Optionally, portions of insurance related database 270 maybe associated with memory modules that are separate from one another,such as a memory 220 of the vehicle in-cabin device 205.

Weather conditions include high glare, foggy glass, heavy traffic,adverse weather, etc.

Turning to FIG. 3A, a vehicle device 300 a is depicted. The vehicledevice 300 a may be similar to, for example, the vehicle device 205 ofFIG. 2. The vehicle device 300 a may include a vehicle deviceregistration module 310 a, a reference image data receiving module 315a, an image sensor data receiving module 320 a, a geographic informationsystem (GIS) data receiving module 325 a, a compass data receivingmodule 327 a, a vehicle device location/orientation module 329 a, aday/time data receiving module 330 a, a skeletal pose data generationmodule 335 a, a vehicle telemetry system data receiving module 340 a, adriver action prediction data generation module 345 a, a driver actiontime stamp data generation module 350 a, a driver action time stamp datatransmission module 355 a, a driver warning generation module 360 a, anda report generation module 365 a stored on a memory 305 a as, forexample, computer-readable instructions.

With reference to FIG. 3B, a vehicle device 300 b is depicted. Thevehicle device 300 b may be similar to, for example, vehicle device 205of FIG. 2. The vehicle device 300 b may include a previously classifiedimage data receiving module 310 b, a current image data receiving module315 b, and a vehicle driving environment determination module 320 bstored on a memory 305 b as, for example, computer-readableinstructions.

Turning to FIG. 4 a remote computing device 400 is depicted. The remotecomputing device 400 may be similar to the remote computing device 210of FIG. 2. The remote computing device 400 may include a reference imagedata transmission module 410, a driver action time stamp data receivingmodule 415, a driver action time stamp data analysis module 420, areport generation module 425, and a driver action time stamp datastorage module 430 stored on a memory 405.

With reference to FIG. 5A, a flow diagram for an example method ofregistering a vehicle device (e.g., vehicle device 205, 300 a, 300 b)within a vehicle 500 a is depicted. The method 500 a may be implementedby a processor (e.g., processor 225) executing, for example, a portionof the modules 310 a-365 a of FIG. 3A. In particular, the processor 225may execute a vehicle device registration module 310 a and a referenceimage data receiving module 315 a to cause the processor 225 to acquireimage data from, for example, an image sensor (e.g., image sensor 265,270 of FIG. 2) (block 505 a). The processor 225 may further execute thevehicle device registration module 310 a to cause the processor 225 toanalyze the image sensor data to determine reference position of thevehicle device 205, 300 a, 300 b (block 510 a). The processor 225 mayfurther execute the vehicle device registration module 310 a to causethe processor 225 to store data representative of the determinedreference position of the vehicle driver (block 515 a). The method 500 amay be implemented, for example, in response to a driver of a vehicleplacing a vehicle device 205, 300 a, 300 b within an associated vehicle(e.g., a driver may place the vehicle device 205, 300 a, 300 b on a dashof the vehicle near a passenger side A-pillar). Thereby, a genericvehicle module 205, 300 a, 300 b may be installed by a vehicle driver inany vehicle.

Vehicle driver postures may be rotated and scaled to be standardized (ornormalized) vehicle device 205, 300 a, 300 b locations within a vehicleand standardized (or normalized) to an average human (i.e., applicableto all drivers). Subsequent to being registered within a given vehicle,a vehicle device 205, 300 a, 300 b may use image sensors 265, 270 todetect driver movements and record/categorize distinct driver postures(e.g., skeletal diagrams 125, 150, 155, 160, 165, 170. The methods andsystems of the present disclosure may present results in two ways: 1)via detailed report of different postures; and 2) via graphicalrepresentation of the postures detected with timeframe (e.g., as inreport 100 of FIG. 1).

With reference to FIG. 5B, a flow diagram for an example method ofgenerating data representative of an inferred vehicle drivingenvironment 500 a is depicted. The method 500 a may be implemented by aprocessor (e.g., processor 225) executing, for example, a portion of themodules 310 b-320 b of FIG. 3B. In particular, the processor 225 mayexecute the previously classified image data receiving module 310 b tocause the processor 225 to, for example, receive previously classifiedimage data (block 505 b). The previously classified image data may be,for example, representative of images and/or extracted image featuresthat have been previously classified as being indicative of degrees ofvehicle operator risk. More particularly, the previously classifiedimage data may include images and/or extracted image features that havepreviously been classified as being representative of a vehicle operatorusing a cellular telephone, a vehicle occupant looking out a vehicleside window, a vehicle occupant adjusting a vehicle radio, a vehicleoccupant adjusting a vehicle heating, ventilation and air conditioningsystem, two vehicle occupants talking with one-another, a vehicleoccupant reading a book or magazine, a vehicle occupant putting onmakeup, a vehicle occupant looking at themselves in a mirror, etc.Alternatively, or additionally, the previously classified image datamay, for example, be representative of known vehicle occupantlocations/orientations, known cellular telephone locations/orientations,known vehicle occupant eye locations/orientations, known vehicleoccupant head location/orientation, known vehicle occupant handlocation/orientation, a known vehicle occupant torsolocation/orientation, a known seat belt location, a known vehicle seatlocation/orientation, etc.

The processor 225 may execute the current image data receiving module315 b to cause the processor 225 to, for example, receive current imagedata (block 510 b). For example, the processor 225 may receive currentimage data from at least one vehicle sensor (e.g., at least one of acompass sensor 327, a GPS sensor 329, an image sensor 336, 337, aninfrared sensor 341, 342, an ultrasonic sensor 346, 347, and/or amicrophone 351, 352). The current image data may include images and/orextracted image features that are representative of a vehicle occupantusing a cellular telephone, a vehicle occupant looking out a vehicleside window, a vehicle occupant adjusting a vehicle radio, a vehicleoccupant adjusting a vehicle heating, ventilation and air conditioningsystem, two vehicle occupants talking with one-another, a vehicleoccupant reading a book or magazine, a vehicle occupant putting onmakeup, a vehicle occupant looking at themselves in a mirror, etc.Alternatively, or additionally, the current image data may, for example,be representative of vehicle occupant locations/orientations, cellulartelephone locations/orientations, vehicle occupant eyelocations/orientations, vehicle occupant head location/orientation,vehicle occupant hand location/orientation, a vehicle occupant torsolocation/orientation, a seat belt location, a vehicle seatlocation/orientation, etc.

The processor 225 may execute the vehicle driving environmentdetermination module 320 b to cause the processor 225 to, for example,infer a vehicle driving environment (block 515 b). For example, theprocessor 225 may compare the current image data with the previouslyclassified image data and may determine that a current image and/orextracted image feature is representative of one of the previouslyclassified images and/or extracted image features. A vehicle drivingenvironment may be inferred using, for example, a probability functionwhere each term may be a weighted factor derived from image data, andmay include images and/or extracted image features that arerepresentative of a vehicle occupant using a cellular telephone, avehicle occupant looking out a vehicle side window, a vehicle occupantadjusting a vehicle radio, a vehicle occupant adjusting a vehicleheating, ventilation and air conditioning system, two vehicle occupantstalking with one-another, a vehicle occupant reading a book or magazine,a vehicle occupant putting on makeup, a vehicle occupant looking atthemselves in a mirror, vehicle occupant locations/orientations,cellular telephone locations/orientations, vehicle occupant eyelocations/orientations, vehicle occupant head location/orientation,vehicle occupant hand location/orientation, a vehicle occupant torsolocation/orientation, a seat belt location, a vehicle seatlocation/orientation, etc. As a specific example, if the current imagedata has a higher likelihood of being representative of a vehicleoccupant using a mobile telephone, the inferred vehicle drivingenvironment may include use of a mobile telephone. When the currentimage data has a higher likelihood to be representative of the vehicleoccupant looking out a side window, the inferred vehicle drivingenvironment may include an object of interest out the side window. Anygiven image or image feature may be weighted individually based upon,for example, a likelihood that the particular image or image feature isrepresentative of a particular vehicle driving environment.

Systems and methods of the present disclosure may include detecting,transmitting, and categorizing in aggregate. There may be times when thesystem encounters previously-unclassified behaviors. For example, adevice may detect driver movements from the current image data. Thedevice may attempt to classify the current image data topreviously-classified image data. Based on the uniqueness of the currentimage data, the device may determine that the probability of a match toa known behavior is below an acceptable threshold. The system onboardthe individual device may create a sample of the 3D or 2D image data andstores on the device storage medium. When the behavior logs are uploadedto an external server, the sample image data of the unique behavior maybe uploaded. Thereby, at a central data repository, a sample of a uniquebehavior may be collected along with other samples of unique behaviors(sent from other individual systems). From the collection of samples,pattern recognition algorithms may be applied in order to categorize thepreviously-uncategorized behaviors. As new categories are developed,these new classifications may be sent to update other devices in thefield so that their classification systems may be even more robust forall the possible behaviors that may occur.

Turning to FIG. 6, a flow diagram of a method of generating datarepresentative of a driver's action along with data representative of atime stamp 600 is depicted. The method 600 may be implemented by aprocessor (e.g., processor 225 of FIG. 2) executing, for example, atleast a portion of the modules 310-365 of FIG. 3. In particular, theprocessor 225 may execute an image sensor data receiving module 320 tocause the processor 225 to receive image sensor data from an imagesensor (e.g., image sensor 265, 270 of FIG. 2) (block 605). Theprocessor 225 may further execute the image sensor data receiving module320 to cause the processor 225 to receive point cloud data from an imagesensor (e.g., image sensor 265, 270 of FIG. 2) (block 610). Theprocessor 225 may execute a skeletal pose data generation module 335 tocause the processor 225 to process the point cloud data through, forexample, a pose estimator to generate skeletal diagram data (block 615).The processor 225 may execute a reference image data receiving module315 to cause the processor 225 to receive data representative of trainedprediction modules (block 620). The processor 225 may execute a driveraction prediction data generation module 345 to cause the processor 225to compare the skeletal diagram data with the trained prediction models(block 620). The processor 225 may execute a day/time data receivingmodule 330 to cause the processor 225 to receive data representative ofa day and/or time associated with a particular drive day/time (block625) The processor 225 may execute a driver action time stamp datageneration module 350 to cause the processor 225 to generate datarepresentative of driver actions along with a timestamp of the actionbased on the driver action data generated in block 620 and further basedon the data representative of the day/time (block 625). The processor225 may execute a driver action time stamp data transmission module 360to cause the processor 225 to transfer the driver action time stamp datato, for example, a driver's cellular telephone via, for example, aBluetooth communication (e.g., wireless transceiver 275 of FIG. 2). Themethod 600 may use skeleton tracking and face tracking technologies toidentify different driver postures. Driver joints data points (e.g.,joints data points 1806-1813 of FIG. 18) may be clustered to createentries which represent a unique driver posture. These postures may thenbe used for making predictions about the subject's driving habits.

In order to infer a type of roadway, traffic condition, and/or weathercondition (i.e., collectively the driving environment), current imagedata may be compared to previously-classified image data. For example,the system may detect whether or not the driver was adjusting AC/heatwhile, for example, driving in heavy traffic and/or light traffic.Accordingly, driver movement may be used to determine roadway type—suchas on-ramps, residential street, highways, intersections, stop 'n gofreeways, roundabouts, speed bumps, waiting at stoplight, crosswalks,parking lots, etc. A known steering wheel orientation (e.g., steeringwheel rotation), known body position relative to fixtures of the vehicle(e.g., relative the roof), known locations/orientations of controllevers near the steering wheel (including the windshield wiper controls,turn signal controls, etc.) may be used to determine roadway type. Headturns, head checks, various degrees of applying steering torque/steeringinputs, use of turn signals, shifting gears (e.g., for either manual orautomatic transmission.), head lurching forward due to rapiddeceleration, head lurching backward due to rapid acceleration, verticaloscillations of the head/torso (e.g., over a speed bump, speed hump,rumble strips, intentionally bumpy roadway feature, or potholes), wavingor signaling to other road users that they have right-of-way, waving orsignaling to other road users that they do not have the right-of-way,adjusting the sun visor, looking at sideview mirrors, looking atrear-view mirrors, activating wiper controls, wiping the interior sideof the windshield.

With reference to FIG. 7, and for prototype purposes, the system mayimplement a method to make predictions for a single driver 700. Themethod 700 may be implemented by a processor (e.g., processor 225 ofFIG. 2) executing, for example, a portion of the modules 310-365 of FIG.3. In particular, the processor 225 may execute an image sensor datareceiving module 320 to cause the processor 225 to collect image data(block 705). The processor 225 may execute a skeletal pose datageneration module 335 to cause the processor 225 to generate clusterdata (block 710). The processor 225 may execute a driver actionprediction data generation module 345 to predict driver's actions (block715).

Turning to FIG. 8, a flow diagram for an example method of registering(or training) a vehicle device (e.g., vehicle device 205, 300) in avehicle 800. The method may be implemented by a processor (e.g.,processor 225 of FIG. 2) executing, for example, at least a portion ofthe modules 310-365 of FIG. 3. The method 800 may include receiving datapoints for a driver's skeletal diagram (block 805), initiating sensorsand related programs (block 810), setting a sensor range to “near mode”(block 815), setting positioning to a “seated mode” (block 820), andinstructing a driver on proper position for calibration (block 825)(e.g., driver should lean forward or move their hands/body (block 826)).The method 800 may also include polling the sensors (e.g., image sensors265, 270) for driver initial position (block 830) and obtaining tentracked points (e.g., points 1806-1813 of FIG. 18) (block 835). Themethod may further include instructing a driver to move to a normalseated position (block 840) and storing vehicle device registration data(block 845).

With reference to FIG. 9, a flow diagram for a method categorizingvarious driver's joints points (e.g., points 1806-1813 of FIG. 18) 900is depicted. The method 900 may include registering initial data pointsof a driver's skeleton diagram (block 905), saving all ten tripletsassociated with a driver's skeleton diagram and associated timestamp(block 910), finding nearest points for each point (block 915) (e.g.,select nearest two vertical and nearest two horizontal points (block916)). The method 900 may also include categorizing the highest pointsas a drivers head (e.g., point 1807 of FIG. 18) (block 920),categorizing the lowest two points as the driver's hands (e.g., points1811, 1813 of FIG. 18) (block 925), and storing the categorized points(block 930).

Turning to FIG. 10, a flow diagram for an example method of predictingdriver actions 1000 is depicted. The method 1000 may include trackingchanges in the skeleton data points which are different than initialdata points and record the changes in a database (block 1005),calculating average depth of ten initial points (block 1010),calculating variability percentage (block 1015) (e.g., variabilitysensitivity may differ depending on point and algorithms (block 1016)),draw ranges for ten joint positions (block 1020), and determine if antrip ended (block 1025). If the trip is determined to have ended (block1025), the method includes saving the last position and ending themethod 1000 (block 1030). If the trip is determined to not have ended(block 1025), the method 1000 checks a driver's current position vs.last logged position (range) (block 1035), and determines whether thedriver's current position is new (block 1040). If the driver's currentposition is determined to be new (block 1040), the method 1000 saves allten triplets and timestamps the triplets (block 1045), and then returnsto block 1020. If the driver's current position is determined to not benew (block 1040), the method 1000 returns to block 1035.

BR1 and TR1.1, 1.2 and 1.3 may be used to identify a new driver (e.g.,an algorithm for recognizing the driver being a new driver). The systemmay use the detailed algorithm mentioned as described in FIGS. 8-10. BR2and TR2.1, 2.2 and 2.3 may be used to track movement of driver's upperbody (e.g., an algorithm for tracking the movement of the driver's upperbody is detailed in FIGS. 8-10). BR3 and TR3.1, 3.2 and 3.3 may be usedto log driver's clearly distinct postures at different times (e.g., analgorithm is to identify and log distinct postures from the movementstracked as part of BR2). The methods and systems of the presentdisclosure may be implemented using C++. Associated applicationprogramming interfaces (APIs) and software development kits (SDKs) maysupport these platforms. Source code for the system may be controlledwith, for example, versioning software available from Tortoise SVN.

With reference to FIG. 11, a sequence diagram for generating a vehiclein-cabin insurance risk evaluation report 1100 is depicted. A reportgenerator 1105 may record/log a trigger 1111 at instance 1110. A datastream reader 1115 may identify a driver 1120 and record/log a trigger1121. A data manipulation 1125 may match/create and entry 1126 andreturn a driver ID 1127. The data stream reader 1115 may read imagesensor data 1130 and record/log a trigger 1131. The data manipulation1125 may store snapshot data 1135 and record/log a trigger 1136. Clusterdata 1140 may match a snapshot with an already registered cluster 1145and may update cluster details 1146 at instance 1150. The reportgenerator 1105 may get data and create a report 1156 at instance 1155.The data manipulation 1125 may return report data 1161 at instance 1160,and the report generator 1105 may print the report 1165.

Turning to FIG. 12, a detailed entity relationship (E-R) diagram 1200 isdepicted. As depicted in FIG. 12, a driver 1230 and a device 1240 may beconnected to a has info block 1205. The driver 1230 may be connected toa name 1231, a driver ID 1232, position coordinates 1233 (e.g., a face),a time stamp 1235, and a device ID 1236. The Device may be connected toa device ID 1241, a model 1242, and a manufacturer 1243. The driver 1230and a ride 1245 may be connected to a takes block 1210. The ride 1245may be connected to a ride ID 1246, an end time 1247, a vehicle 1248(e.g., a car), a risk 1249, and a start time 1250. The ride 1245 andsnapshots 1255 may be connected to a contains block 1215. The snapshots1255 may be connected to a snapshots ID 1256, a ride ID 1257, and a timestamp 1258. The snapshots 1255 and a posture 1260 may be connected to alabel with block 1220. The posture 126 may be connected to a posture ID1261 and a time stamp 1262. The snapshots 1255 and joints 1275 may beconnected to a consists of block 1225. The joints 1275 may be connectedto a x-value 1266, a y-value 1267, a z-value 1268, a snapshot ID 1269,and a joint ID 1270.

With reference to FIG. 13, a method for creating a read-only databaseaccount 1300 is depicted. A database layer 1300 may be developed inMySQL server. The method 1300 may start (block 1305). All the rows inthe database may be labeled as belonging to distribution G₁ (block1310). The database creation 1300 may restart from a first row (block1315). A probability that the row (1) dataset falls under distributionG₁ is obtained (blocks 1320, 1321). A probability that the row (2)dataset falls under distribution G₁ is obtained (blocks 1325, 1326). Acategorization process 1330 may include finding a maximum probability1331. If a probability that the row (1) is found to be highest (block1330), the row is labeled with distribution G₁ (block 1332). If aprobability that the row (2) is found to be highest (block 1330), a newG₂ is created and the row is labeled with distribution G₂ (block 1333)and the updated G₂ and associated parameters are stored in the databaseas a cluster (block 1334). The method 1300 proceeds to the next row inthe database (block 1335). A probability that the row (1) dataset fallsunder distribution G₁, G₂, . . . G_(n) is obtained (blocks 1340, 1341).A probability that the row (2) dataset falls under a new distribution isobtained (blocks 1345, 1346). A categorization process 1350 may besimilar to the categorization process 1330. A determination as towhether the current row is the end of the database is made (block 1355).If the current row is determined to not be the last row (block 1355),the method 1300 returns to block 1335. If the current row is determinedto be the last row (block 1355), the method 1300 proceeds to determineif the process discovered a new G_(s) or updated existing ones (block1360). If the process is determined to have discovered a new G_(s) orupdated existing ones (block 1360), the method 1300 returns to block1315. If the process is determined to not have discovered a new G_(s) orupdated existing ones (block 1360), all the existing clusters may beidentified and results may be printed (block 1365) and the method 1300ends (block 1370).

Turning to FIG. 14, a high-level block diagram of a developmentenvironment 1400 is depicted. The development environment 1400 mayinclude an image sensor 1410 and a server 1405 hosting a database 1415and VC++ implementation for collecting and clustering data. A userinterface of the development environment may have a model car, parkedcar, or a dummy setup for a user to act as a driver. The system mayanalyze the movements of the driver during a trial period and maygenerate two sets of reports: 1) A live video of the skeleton frameswith start, end and total time for the ride demo; and 2) A report shownalso as charts of different postures and time spent for each posture asdepicted, for example, in FIG. 1. The development environment is focusedon building a working model of the concept. The end-to-end system usesMicrosoft Kinect, Microsoft Visual Studio C++, MySQL database andMicrosoft Windows as platform.

With reference to FIG. 15, a system diagram 1500 is depicted for adevelopment environment of FIG. 14. The system 1500 may include HTMLand/or GUI APIs 1505, a MYSQL database 1510, and SigmaNI+Open NI SDKs1515. The system diagram 1500 depicts different C++ modules fordifferent functionalities of the project. The system 1500 may alsoinclude an AppComponents::iDataManipulation component 1525 to interactwith the MYSQL database 1510. All other components may use APIs in thiscomponent to interact with MYSQL database. The system 1500 may furtherinclude an AppComponents::iReadDataStream component 1535 to interactwith Sensor hardware via KinectSDK middleware (e.g., SigmaNI+Open NISDKs 1515). The iReadDataStream component 1535 may read a data streamfrom the sensor (e.g., image sensor 260, 265 of FIG. 1) and may storethe data structure in a Snapshot table for further clustering andprocessing. The system 1500 may also include anAppComponents::iClusterData component 1530 that may read snapshot datastored by the iReadDataStream component 1535 and may cluster the data toidentify driver postures. The AppComponents::iClusterData component 1530may begin to function once new data is stored in a database by theiReadDataStream component 1535. The system 1500 may further include anAppComponents::iPredictionModule component 1540 that may function as aprediction engine, and may have algorithms to implement driving habitanalysis for the captured data. The system 1500 may also include anAppComponents::iReportGenerator component 1520 that, for successfuldemonstration, a report will be generated. TheAppComponents::iReportGenerator component 1520 may have APIs to read thedata via iDataManipulation component 1525 from the database and generatereport. This component will also display the live video of theparticipant on the screen. For the live video, it will capture the datadirectly from iReadDataStream component 1535.

An AppComponents::iDataManipulation 1525 may include input related tobusiness objects acquired from or required by various business methodsin other components. Output/Service may be provided for business objectsextracted from a database via data access objects and methods. Dependingon which component is calling, this component may have generic andclient specific APIs for serving various business objects.Component/Entity process: Data connection; Connection pool; DAOs forbelow entities; Driver; Snapshot Object; RideDetails; andPosturesDetails. Constraints may include initial connection pool size often and max size may be thirty.

An AppComponents::iReadDataStream component 1535 may include input foran event to start and stop reading a video and sensor data stream fromhardware. A SDK APIs may be used for reading skeleton, face and handtracking data. Output/Service may be provided via snapshot objects andrelevant joints coordinates may be output and stored in the databaseusing Data manipulation component 1525. Live data may be transported toReportGenerator component 1520. Component/Entity process may work as abatch process to start and stop logging the read data in the databasewhen triggered. The component also needs to be able to transmit livedata to iReportGenerator component 1520 to show it on screen.Constraints may include appropriate buffering and error handling whichmay be done, to make sure appropriate error messages aredisplayed/captured for downstream components.

An AppComponents::iClusterData component 1530 may input snapshot dataread from iReadDataStream and a database. Output/Service may be providedand assign a postureID to a snapshot and update the posture-database.Component/Entity process may include: Retrieving snapshot and postureinformation from database; Matching snapshots with postures; Insertingnew snapshot/posture information to database; Implementations ofunsupervised clustering algorithms. Constraints may include a number ofclusters generated has a limit.

An AppComponents::iPredictionModule component 1540 may serve to take indata from a database, and turn the data into information to leverage.The AppComponents::iPredictionModule component 1540 may identify riskydrivers, review their in-cabin driving habits, and eventually act tocurb these risky habits. This section explains how the data may bemodeled to better understand which factors correlate to a defined riskmetric and how certain behavior patterns contribute to a higherinsurance risk rating.

An AppComponents::iReportGenerator 1520 may include input informationtaken from a database, the ten coordinates taken from the data streamduring a demo, a start time, an elapsed time and some dummy information.Output/Service may be provided including a video of skeleton frames withstart time and elapsed time and a report that displays charts that mayillustrate what happened during the demo. The report may include apicture of the driver, the driver's name, and the range of movement ofmost distinct postures. The report may also have a line graph and a bargraph that show how much time the driver spent in each posture. Thereport may display the skeleton coordinates of the five postures thedriver was in the most along with the time and number of occurrences ofeach. Component/Entity process may include: a Generator; a Report; aVideo; a DAOs for below entities; a Ride; a Posture and a Joint.Constraints may include a demo that may have at least five differentpostures. Number of postures and number of occurrences should not exceedmax array length.

Turning to FIG. 16, a system for generating data representative of avehicle in-cabin insurance risk evaluation 1600 is depicted. The system1600 may include a plurality of vehicle devices 1605 communicativelycoupled to a data processing, filtering and load balancing server 1615via a wireless webservice port 1610 to send and receive data. The system1600 may also include a database server 1620 and database 1621 to storein-cabin data, and an algorithm server 1625 to continuously refinealgorithm data and an associated prediction engine. When multiplesensors are used, a SigmaNI wrapper may be used as an abstraction layerfor code. This may ensure that if a sensor is changed, or differentsensors are user, minimal code changes are required.

With reference to FIG. 17, when SigmaNI is not an approved software, animplementation 1700 may directly interact with a SDK 1710 to get thedriver data from a sensor 1705 for generation data representative ofvehicle in-cabin insurance risk evaluations 1715 and storing the data ina database 1720. The system 1700 may use sensors (e.g., image sensor260, 265 of FIG. 1) for detecting the driving postures, such as providedby Microsoft Kinect for windows, Carmine 1.09 and/or Softkinect DS325.The following SDKs may be used with the above hardware: a Kinect SDK, anOpenNI, a Softkinect SDK and/or a SigmaNI.

Turning to FIG. 18, a posture (or skeletal diagram) 1800 may include tenjoint positions 1806-1813 for a driver's upper body 1805. An associatedcluster may include ten rounds with radius 10 cm and centered at ten3-dimensional points. A match (posture p, cluster c) may return true ifall the ten joint positions of the posture are contained in the tenballs for the cluster accordingly, otherwise returns false. A distanceof two points may be measured using a Euclidean distance. For example,given a pair of 3-D points, p=(p1, p2, p3) and q=(q1, q2, q3): distance(p, q)=sqrt((p1−q1){circumflex over ( )}2+(p2−q2){circumflex over( )}2+(p3−q3){circumflex over ( )}2). A cube in 3-dimensional consistsall points (x, y, z) satisfy following conditions: a<=x<=b, c<=y<=d,e<=z<=f, where b−a=d−c=f−e. When initialization: a first cluster may bedefined by the ten joint positions of the first posture. A cluster maybe added to the initial cluster list, denote CL Loop: for each ofsubsequent postures, say P, for each cluster in CL, say C, if Match (P,C): Label P with C, break, End For. If P does not have a cluster label,create a new cluster C′ and add C′ to CL—End For and BR4 [TR 4.1, 4.2,4.3 and 4.4] Create risk profile of the driver.

With reference to FIG. 19, an object design for a detailed entityrelationship (E-R) diagram 1900 is depicted. An associated databaselayer may be developed in MySQL server. The entity relationship 1900 mayinclude a device 1905 connected to a driver 1910, connected to a ride1920, connected to a snapshot 1930 which is connected to both joints1935 and a posture 1945. The device 1905 may include a device ID 1906, amodel, 1907 and a manufacturer 1908. The driver 1910 may include adriver ID 1911, a device ID 1912, a name 1913, face coordinates 1914,and a time stamp 1915. The ride 1920 may include a ride ID 1921, adriver ID 1922, a start time 1923, an end time 1924, a car 1925, and arisk 1920. The snapshot may include a snapshot ID 1931, a ride ID 1932,and a time stamp 1933. The joints 1935 may include a joint ID 1936, asnapshot ID 1937, a x-value 1938, a y-value 1939, and a z-value 1940.The posture 1945 may include a posture ID 1946, a snapshot ID 1947, anda time stamp 1948.

Turning to FIG. 20, a class diagram 2000 may include a BaseDAO 2005, aDeviceDAO 2010, a DriverDAO 2015, a SnapshotDAO 2015, a JointDAO 2035, aPostureDAO 2045, and a RideDAO 2050. The BaseDAO 2005 may include a con:DBConnection 2006 and a # getConnection( ) 2007. The DeviceDAO 2010 mayinclude a DeviceID:String 2011, a Model: String 2012, aManufacturer:String 2013, and a getters/setters 2014. The DriverDAO 2015may include a DriverID:String 2016, a Name:String 2017, aFaceCoordinates:Array(int 100) 2018, a Device Obj:Device DAO 2019, atimestamp:timestamp 2020, and a getters/setters 2012. The SnapshotDAO2015 may include a SnapshotID:String 2026, a RideID:String 2027, aTimeStamp:timestamp 2028, a Joints:Array (jointDAO 10) 2029, and agetters/setters 2030. The JointDAO 2035 may include a JointID:String2036, a X:int 2037, a Y:int 2038, a Z:int 2039, and a getters/setters2040. The PostureDAO 2045 may include a PostureID:String 2046, aSnapshotID:String 2047, a getters/setters 2048, and a fetTopPostureIDs(Postures) 2049. The RideDAO 2050 may include a RideID:String 2051, aDriverObj:DriverDAO 2052, a StartTime:timestamp 2053, anEndTime:timestamp 2054, and a getters/setters 2055.

With reference to FIG. 21, a class diagram 2100 may include aReadSensorData component 2105, a ReadKinectSensorData component 2115,and a ReadCarmineSensorData component 2120. The ReadSensorData component2105 may include a Snapshot:SnapshotDAO 2106, an initialize( ) parameter2107, a readDataStream( ) parameter 2108, a saveSnapshot( ) parameter2109, a transmitLiveOparameter 2110, and agetters/setter parameter 2111.The ReadKinectSensorData component 2115 may include an initializeKinect() parameter 2116. The ReadCarmineSensorData component 2120 may includean initializeCarmine( ) parameter 2121.

Turning to FIG. 22, a class diagram 2200 may include a ClusterDatacomponent 2205, a Posture component 2225, a Snapshot component 2230, anda Joint component 2235. The ClusterData component 2205 may include asurSS:SnapShot 2206, a postures:ArrayList(Posturess) 2207, a postureIDinteger 2208, a Match_Posture( ) 2209, a Update_DB( ) 2210, and agetters/setters 2211. The Posture component 2225 may include apostureID:integer 2226, a Cneters:Array(Joint) 2227, a Radius:Number2228, and a getters/setters 2229. The Snapshot component 2230 mayinclude a SnapshotID: Integer 2231, a Joints:Array(Joint) 2232, atimestamp:Number 2233, and a getters/setters 2234. The Joint component2235 may include a x:Number 2236, a y:Number 2237, a z:Number 2238, anda getters/setters 2239.

With reference to FIG. 23, a class diagram 2300 may include aReportGenerator component 2305, a Report component 2310, and a Videocomponent 2320. The ReportGenerator component 2305 may include aReport:Report 2306, a Video:Video 2307, and a getters/setters 2308. TheReport component 2310 may include a DriverName:String 2311, aRideObj:RideDAO 2312, a getters/setters 2313, a drawLineGraph(RideObj)2314, a drawBarGraph(RideObj) 2315, and a drawTopPostures(RideObj) 2316.The Video component 2320 may include a CurrentPosture:PostureDAO 2321, aStartTime:timestamp 2322, a CurrentTime:timestamp 2323, agetters/setters 2324, a displayPosture(CurrentPosture) 2325, and adisplayTimes (starTime, currentTime) 2326.

A car-sharing insurance product could more specifically insure thedriver, regardless of the car. Traditional underwriting looks at thedriver-vehicle combination. What car-sharing would allow you to do is tomore heavily weight the risk of the driver alone. The methods andsystems of the present disclosure may allow car-sharing to get that riskinformation on the driver and carry it forward to whatever car they use.This would be tailored for that particular driver's behavior, ratherthan demographic and vehicle-use factors. This would allow certaincar-sharing entities to have a cost advantage. If they are paying lessinsurance—or more specific insurance—they could pass those savings totheir customers and have a retention strategy.

The methods and systems of the present disclosure may allow foremergency responders by, for example, using gesture recognition systemsfrom an aftermarket/insurance device in order to provide an estimate tofirst responders about the severity of the crash and what kinds ofresources/equipment/expertise is required in order to extricate. Usingthe gesture recognition systems from an aftermarket/insurance device inorder to provide an estimate to first responders about the severity ofthe crash and what kinds of resources/equipment/expertise is required inorder to triage—have some idea of what emergency medical needs could beupon arrival. Since the “golden hour” is so critical, and it's notalways known how much of that hour has already expired, even apreliminary or broad clue could be helpful in the triage process. Theaftermarket gesture recognition device is already operating at the timeof the crash. It is collecting data about the driver's position/postureand the location of the arms relative to the body and structures in thevehicle (i.e. the steering wheel). Accelerometers in the device are ableto recognize that a crash has occurred (if a pre-determined accelerationthreshold has been reached). Upon crash detection the device couldtransmit via the driver's phone (which is already connected viaBluetooth) or perhaps transmit using an onboard transmitter that usesemergency frequencies (and therefore does not require consumer to payfor data fees). Using gesture recognition from any original equipment oraftermarket gesture tracking device, whether or not for insurancepurposes.

The methods and systems of the present disclosure may allow forTransition from Automated to Manual Driving Mode in the case of vehicleautomation systems operating the piloting functions with the human in asupervisory role. The vehicle encounters a situation where it needs totransfer control to the driver, but the driver may or may not be readyto resume control. The methods and systems of the present disclosure mayallow gesture recognition systems, or any gesture recognition system, tobe used to determine if the driver is ready to resume control. If he/sheis not ready, then get his/her attention quickly. The gesturerecognition would be used to ascertain whether the driver is ready toresume control by evaluating the driver's posture, the location ofhands, the orientation of head, body language. Use machine learning toevaluate driver engagement/attention/readiness-to-engage based on thosevariables. The gesture recognition could be any original in-vehicleequipment or aftermarket device.

The methods and systems of the present disclosure may distinguishbetween Automated and Manual driving modalities for variable insurancerating for a scenario where there are many vehicles that are capable ofautomatically operating the piloting functions, and are capable of thedriver manually operating the piloting functions. The driver can electto switch between automated and manual driving modes at any point duringa drive. Gesture recognition would be utilized to distinguish whether adriver is operating the vehicle manually, or whether the vehicle isoperating automatically. This could be determined through either OEM oraftermarket hardware. The sensors and software algorithms are able todifferentiate between automatic and manual driving based on handmovements, head movements, body posture, eye movements. It candistinguish between the driver making hand contact with the steeringwheel (to show that he/she is supervising) while acting as a supervisor,versus the driver providing steering input for piloting purposes.Depending on who/what is operating the vehicle would determine whatreal-time insurance rates the customer is charged.

The methods and systems of the present disclosure may provide a tool formeasuring driver distraction where gesture recognition may be used toidentify, distinguish and quantify driver distracted for safetyevaluation of vehicle automation systems. This would be used to definemetrics and evaluate safety risk for the vehicle human-machine interfaceas a whole, or individual systems in the case where vehicles haveautomation and vehicle-to-vehicle/vehicle-to-infrastructurecommunication capabilities. Where Vehicle automation: the vehicle iscapable of performing piloting functions without driver input. WhereVehicle-to-vehicle/vehicle-to-infrastructure communication: the vehicleis capable of communicating data about the first vehicle dynamics orenvironmental traffic/weather conditions around the first vehicle. Forany entity looking to evaluate the safety or risk presented by a vehiclewith automated driving capabilities, DRIVES gesture recognition could beuseful to quantify risk presented by driver distraction resulting fromany vehicle system in the cabin (i.e. an entertainment system, a featurethat automates one or more functions of piloting, a convenience system).With the rise of vehicle automation systems and capabilities, tools willbe needed to evaluate the safety of individual systems in the car, orthe car as a whole. Much uncertainty remains about how these systemswill be used by drivers (especially those who are not from the communityof automotive engineering or automotive safety). Determining whetherthey create a net benefit to drivers is a big question. The methods andsystems of the present disclosure may allow gesture recognition could beused to identify the presence of distracted driving behaviors that arecorrelated with the presence of vehicle automation capabilities. Thedistracted could be quantified by duration that the driver engages incertain behaviors. Risk quantification may also be measured by weightingcertain behaviors with higher severity than other behaviors, so theduration times are weighted. Risk quantification may also differentiatesubcategories of behaviors based on degree of motion of hands, head,eyes, body. For example, The methods and systems of the presentdisclosure may distinguish texting with the phone on the steering wheelfrom texting with the phone in the driver's lap requiring frequentglances up and down. The latter would be quantified with greater risk interms of severity of distraction. The purpose of this risk evaluationcould be for reasons including but not limited to adhere to vehicleregulations, providing information to the general public, vehicle designtesting or insurance purposes.

This detailed description is to be construed as exemplary only and doesnot describe every possible embodiment, as describing every possibleembodiment would be impractical, if not impossible. One may be implementnumerous alternate embodiments, using either current technology ortechnology developed after the filing date of this application.

What is claimed is:
 1. A device for inferring a vehicle drivingenvironment, the device comprising: a previously classified image datareceiving module stored on a memory that, when executed by a processor,causes the processor to receive previously classified image data from atleast one previously classified image database, wherein the previouslyclassified image data is representative of previously classified vehicleoccupant actions; a current image data receiving module stored on amemory that, when executed by a processor, causes the processor toreceive current image data from at least one vehicle interior sensor,wherein the current image data is representative of current vehicleoccupant action; and a vehicle driving environment determination modulestored on a memory that, when executed by a processor, causes theprocessor to infer a vehicle driving environment based on a comparisonof the current image data with the previously classified image data,wherein the vehicle driving environment is associated with at least onedistracted driving behavior, and wherein a risk associated with the atleast one distracted driving behavior is quantified based on a weightedfactor associated with a first duration of time of a first vehicleoccupant behavior relative to a second duration of time of a secondvehicle occupant behavior.
 2. The device as in claim 1, wherein the atleast one vehicle interior sensor is selected from: at least one digitalimage sensor, at least one ultra-sonic sensor, at least oneradar-sensor, at least one infrared light sensor, or at least one laserlight sensor.
 3. The device as in claim 1, wherein the previouslyclassified image data is representative of scaled vehicle occupantpostures that are normalized for a range of different drivers.
 4. Thedevice as in claim 1, wherein the previously classified image data isrepresentative of a three-dimensional representation of at least oneoccupant within the vehicle interior.
 5. The device as in claim 1,wherein the current image data is representative of a three-dimensionalrepresentation of at least one occupant within the vehicle interior. 6.The device as in claim 1, wherein the current image data includes imagesand/or extracted image features that are representative of a vehicleoccupant using a cellular telephone, a vehicle occupant looking out avehicle side window, a vehicle occupant adjusting a vehicle radio, avehicle occupant adjusting a vehicle heating, ventilation and airconditioning system, two vehicle occupants talking with one-another, avehicle occupant reading a book or magazine, a vehicle occupant puttingon makeup, or a vehicle occupant looking at themselves in a mirror. 7.The device as in claim 1, wherein the previously classified image dataincludes images and/or extracted image features that have previouslybeen classified as being representative of a vehicle occupant using acellular telephone, a vehicle occupant looking out a vehicle sidewindow, a vehicle occupant adjusting a vehicle radio, a vehicle occupantadjusting a vehicle heating, ventilation and air conditioning system,two vehicle occupants talking with one-another, a vehicle occupantreading a book or magazine, a vehicle occupant putting on makeup, or avehicle occupant looking at themselves in a mirror.
 8. Acomputer-implemented method for inferring a vehicle driving environment,the method comprising: receiving, at a processor of a computing device,previously classified image data from at least one previously classifiedimage database in response to the processor executing a previouslyclassified image data receiving module, wherein the previouslyclassified image data is representative of previously classified vehicleoccupant actions; receiving, at a processor of a computing device,current image data from at least one vehicle interior sensor a currentimage data receiving module, in response to the processor executing acurrent image data receiving module, wherein the current image data isrepresentative of current vehicle occupant actions; and inferring, usinga processor of a computing device, a vehicle driving environment, basedon a comparison of the current image data with the previously classifiedimage data, in response to the processor executing a vehicle drivingenvironment determination module, wherein the vehicle drivingenvironment is associated with at least one distracted driving behavior,and wherein a risk associated with the at least one distracted drivingbehavior is quantified based on a weighted factor associated with afirst duration of time of a first vehicle occupant behavior relative toa second duration of time of a second vehicle occupant behavior.
 9. Themethod as in claim 8, wherein the at least one vehicle interior sensoris selected from: at least one digital image sensor, at least oneultra-sonic sensor, at least one radar-sensor, at least one infraredlight sensor, or at least one laser light sensor.
 10. The method as inclaim 8, wherein the current image data is representative of athree-dimensional representation of at least one occupant within thevehicle interior.
 11. The method as in claim 8, wherein the previouslyclassified image data is representative of a three-dimensionalrepresentation of at least one occupant within the vehicle interior. 12.The method as in claim 8, wherein the previously classified image datais representative of scaled vehicle occupant postures that arenormalized for a range of different drivers.
 13. The method as in claim8, wherein the current image data includes images and/or extracted imagefeatures that are representative of vehicle occupantlocations/orientations, cellular telephone locations/orientations,vehicle occupant eye locations/orientations, vehicle occupant headlocation/orientation, vehicle occupant hand location/orientation, avehicle occupant torso location/orientation, a seat belt location, or avehicle seat location/orientation.
 14. The method as in claim 8, whereinthe previously classified image data includes images and/or extractedimage features that have previously been classified as beingrepresentative of known vehicle occupant locations/orientations, knowncellular telephone locations/orientations, known vehicle occupant eyelocations/orientations, known vehicle occupant headlocation/orientation, known vehicle occupant hand location/orientation,a known vehicle occupant torso location/orientation, a known seat beltlocation, or a known vehicle seat location/orientation.
 15. Anon-transitory computer-readable medium storing computer-readableinstructions that, when executed by a processor, cause the processor toinferring a vehicle driving environment, the non-transitorycomputer-readable medium comprising: a previously classified image datareceiving module that, when executed by a processor, causes theprocessor to receive previously classified image data from at least onepreviously classified image database, wherein the previously classifiedimage data is representative of previously classified vehicle occupantactions; a current image data receiving module that, when executed by aprocessor, causes the processor to receive current image data from atleast one vehicle interior sensor, wherein the current image data isrepresentative of current vehicle occupant actions; and a vehicledriving environment determination module that, when executed by aprocessor, causes the processor to infer a vehicle driving environmentbased on a comparison of the current image data with the previouslyclassified image data, wherein the vehicle driving environment isassociated with at least one distracted driving behavior, and wherein arisk associated with the at least one distracted driving behavior isquantified based on a weighted factor associated with a first durationof time of a first vehicle occupant behavior relative to a secondduration of time of a second vehicle occupant behavior.
 16. Thenon-transitory computer-readable medium as in claim 15, wherein thepreviously classified image data is representative of scaled vehicleoccupant postures that are normalized for a range of different drivers.17. The non-transitory computer-readable medium as in claim 15, whereinthe current image data is representative of a three-dimensionalrepresentation of at least one occupant within the vehicle interior. 18.The non-transitory computer-readable medium as in claim 15, wherein thecurrent image data includes images and/or extracted image features thatare representative of a vehicle occupant using a cellular telephone, avehicle occupant looking out a vehicle side window, a vehicle occupantadjusting a vehicle radio, a vehicle occupant adjusting a vehicleheating, ventilation and air conditioning system, two vehicle occupantstalking with one-another, a vehicle occupant reading a book or magazine,a vehicle occupant putting on makeup, or a vehicle occupant looking atthemselves in a mirror, vehicle occupant locations/orientations,cellular telephone locations/orientations, vehicle occupant eyelocations/orientations, vehicle occupant head location/orientation,vehicle occupant hand location/orientation, a vehicle occupant torsolocation/orientation, a seat belt location, or a vehicle seatlocation/orientation.
 19. The non-transitory computer-readable medium asin claim 15, wherein the previously classified image data includesimages and/or extracted image features that have previously beenclassified as being representative of a vehicle occupant using acellular telephone, a vehicle occupant looking out a vehicle sidewindow, a vehicle occupant adjusting a vehicle radio, a vehicle occupantadjusting a vehicle heating, ventilation and air conditioning system,two vehicle occupants talking with one-another, a vehicle occupantreading a book or magazine, a vehicle occupant putting on makeup, avehicle occupant looking at themselves in a mirror, known vehicleoccupant locations/orientations, known cellular telephonelocations/orientations, known vehicle occupant eyelocations/orientations, known vehicle occupant headlocation/orientation, known vehicle occupant hand location/orientation,a known vehicle occupant torso location/orientation, a known seat beltlocation, or a known vehicle seat location/orientation.
 20. Thenon-transitory computer-readable medium as in claim 15, wherein thepreviously classified image data is representative of athree-dimensional representation of at least one occupant within thevehicle interior.